标题： 高斯混合模型的传感器融合方法 显示英文标题

标题： Sensor Fusion Methods for Gaussian Mixture Models

摘要： 一致性是一种流行的分布式状态估计技术。这种公式化方法允许连接的代理或传感器网络与其直接邻居交换关于一组目标分布的信息，而无需中央节点或层。 我们提出了一个分散的一致性融合技术系统，该系统的目标先验估计是高斯概率密度函数（PDF）的加权混合物，对于以下情况：1）所有代理具有相同的目标高斯混合先验估计，以及2）代理具有不同的目标高斯混合先验估计。对于第二种情况，我们提出了一种公式化方法，融合每个代理的先验估计而不使用局部观测值，使得每个代理在网络中的后验估计相同。 显示英文摘要

摘要： Consensus is a popular technique for distributed state estimation. This formulation allows networks of connected agents or sensors to exchange information about the distribution of a set of targets with their immediate neighbors without the need of a centralized node or layer. We present decentralized consensus-based fusion techniques for a system whose target prior estimates are a weighted mixture of Gaussian probability density functions (PDFs) for the following cases: 1) in which all agents have the same a priori Gaussian mixture estimate of the target, and 2) in which agents have different a priori Gaussian mixture estimates of the target. For the second case, we present a formulation that fuses each agent's a priori estimate without using local observations such that each agent's posterior estimate is the same across the network.